Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling

Abstract

Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling. European Journal of Operational AbstractMany real-world optimisation problems approached by evolutionary algorithms are subject to noise. When noise is present, the evolutionary selection process may become unstable and the convergence of the optimisation adversely affected. In this paper, we present a new technique that efficiently deals with noise in multi-objective optimisation. This technique aims at preventing the propagation of inferior solutions in the evolutionary selection due to noisy objective values. This is done by using an iterative resampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. To achieve an efficient utilisation of resources, the number of samples used per solution varies based on the amount of noise in the present area of the search space. The proposed algorithm is evaluated on the ZDT benchmark problems and two complex real-world problems of manufacturing optimisation. The first realworld problem concerns the optimisation of engine component manufacturing in aviation industry, while the second real-world problem concerns the optimisation of a camshaft machining line in automotive industry. The results from the optimisations indicate that the proposed technique is successful in reducing noise, and it competes successfully with other noise handling techniques.

Evolutionary robust optimization in production planning – interactions between number of objectives, sample size and choice of robustness measure

“…Simulation-based optimization (SBO) is an attractive approach to address this problem, for it can accurately capture the inherent uncertainty and complexity of the manufacturing system, while searching for near-optimal solutions for the problem analysed [21,46,20]. The SBO model consists of a simulation model and an EA coupled in a black-box fashion.…”

The Rolling Tide Evolutionary Algorithm: A Multiobjective Optimizer for Noisy Optimization Problems

“…In Section VI we evaluate our new optimiser along with four other recent noise-tolerant MOEAs [6], [16], [21], [22], on standard test problems modified by the addition of noise. In particular we show that our proposed optimiser performs well in the presence of noise with a wide range of characteristics, including when the noise varies spatially and temporally during the course of the optimisation.…”

“…However, it was not until the early 2000s that a significant number of multi-objective optimisers began to be developed specifically to tackle uncertain optimisation [12], [13], [14], [5], [6], [15], [7], [16], [17], [18], [19], [20], [21], [22], [23]. Typically uncertainty is modelled as noise added to the function evaluations, although recent work has begun looking at the situation where the objective functions themselves are uncertain [24].…”

A Toolkit for Generating Scalable Stochastic Multiobjective Test Problems

“…The problems developed in these studies are deterministic functions that demonstrate differences between the nominal Pareto front and the 'robust' front. Similarly, Goh and Tan [9], Knowles and Corne [12], Syberfeldt et al [18] and Fieldsend and Everson [5] used deterministic test problems with added noise to the objective values to benchmark their algorithms. This type of problem is sufficient for the assumption that the 'true' objective vector is masked by measurement noise.…”

“…Typically, in these studies, symmetric random noise is added to the decision variables [3,7,14,15,16] or the objective functions [5,9,12,18] in a bespoke way that meets the requirements for the specific uncertainties and definitions of robustness being considered. The problem that is used to benchmark every suggested algorithm is tailored to the type of uncertainty and definition of robustness considered in the study.…”